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July 20, 2026
Scientists Create Open Dataset for Studying Concentration
A team of Russian researchers, including scientists from HSE University–St Petersburg, has developed the first open multimodal dataset containing recordings of brain activity, heart function, and video observations to help researchers understand what happens in the human brain during deep concentration. In the future, the dataset could accelerate the development of neural interfaces, rehabilitation technologies, and AI systems. The article has been published in Scientific Data.
July 20, 2026
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Fuad Aleskerov, Tenured Professor and Director of the International Centre of Decision Choice and Analysis at HSE University, together with his colleagues, has developed methods of network analysis in bibliometrics that have made it possible to identify patterns in the appearance and citation of publications in academic journals, as well as their influence on each other. When one or a number of studies are frequently cited by a wide range of journals, this is an indicator that the research is of high quality. By contrast, extensive cross-citation within a limited group of journals increases the likelihood of identifying a network of predatory publications.
July 20, 2026
Scientists Propose Method for More Efficient Resource Use in Machine Learning
An international group of researchers, including mathematicians from the AI and Digital Science Institute at the HSE Faculty of Computer Science, has provided a theoretical justification for a simple and computationally efficient method of estimating uncertainty in Stochastic Gradient Descent (SGD). The paper has been published on the scientific preprint server arXiv.org and presented at AISTATS 2026.

 

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Концепция предотвратимой смертности как основа для разработки мер по её снижению: пример болезней системы кровообращения

Российский кардиологический журнал. 2025. Т. 30. № 10. С. 103–110.
Кобякова О. С., Стародубов В. И., Бойцов С. А., Шляхто Е. В., Деев И. А., Marychev G., Шибалков И. П., Логненко М. М., Перфильева Д. Ю., Соломатников И. А.

This article demonstrates how the concept of preventable mortality can be used to develop mortality reduction measures using cardiovascular disease (CVD) as an example. The measures are structured according to Donabedian model. A Russian methodology for determining preventable mortality, based on international approaches and adapted to Russian healthcare system, was used. In collaboration with federal experts, a list of measures to reduce preventable cardiovascular mortality was developed. To systematize these measures, Donabedian’s triad was used, identifying resources, processes, and performance indicators for each measure. Following activity examples are presented: "health schools" using telemedicine technologies, individual patient information, and stress echocardiography. Their structuring according to the Donabedian model makes it possible to identify the causes of potentially unsatisfactory results and improve their implementation. Integrating the concept of preventable mortality with the Donabedian model ensures a rational selection and evaluation of measures to reduce cardiovascular mortality, as well as the effective allocation of resources. The list of activities requires regular updating, taking into account changes in medical technologies and healthcare system capabilities.

Language: Russian
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Keywords: здравоохранениеpreventionhealthcareболезни системы кровообращенияtreatmentлечениепредотвратимая смертностьпрофилактикаcardiovascular diseasesPreventable mortalityDonabedian modelмодель Донабедиана
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